Besides softmax, output features of the input in representation spaces could also be leveraged to improve the OOD detector.
Some work designed distance-based metrics to distinguish OOD data in representation space. For example,
Mahalanobis [1]} first takes hidden layers of the DNN model as representation spaces, and then
computes Mahalanobis distance to measure how the data belongs to in-distribution in these spaces.
Some work involves additional samples to enhance the power of OOD detection during the DNN model training phase.
Lee aims to train a classifier with a GAN [2]}, [3]} to make the prediction on GAN samples with lower confidence [4]}.
Outlier Exposure (OE) [5]} needs a disjoint OE dataset to mimic the OOD dataset for testing, however the concept of disjoint between OE and OOD is unclear.
To detect data from the specific OOD, the above methods all treat the detection as a binary classification problem so that the samples from that OOD are necessities for training the DNN model or the detector.
